Use of continuous genotypes for genomic prediction in sugarcane

• Published in: The plant genome Vol. 17; no. 1; pp. e20417 - n/a
• Main Authors: Yadav, Seema, Ross, Elizabeth M., Wei, Xianming, Liu, Shouye, Nguyen, Loan To, Powell, Owen, Hickey, Lee T., Deomano, Emily, Atkin, Felicity, Voss‐Fels, Kai P., Hayes, Ben J.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01-03-2024; Wiley
• Subjects: Accuracy; Alleles; Arrays; Bayesian analysis; Cloning; Crops; Diploids; Dosage; Expected values; Genomes; Genomics; Genotype & phenotype; Genotyping; Heterozygotes; Mathematical models; Phenotypic variations; Ploidy; Polyploidy; Prediction models; Single-nucleotide polymorphism; Sugarcane
• ISSN: 1940-3372; 1940-3372
• DOI: 10.1002/tpg2.20417

Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo‐diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele‐dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non‐parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%–7% for CCS and Fiber. The pseudo‐diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc‐cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non‐additive effects might influence these traits. The prevalence of low‐dosage markers in the study may have limited the benefits of approximating allele‐dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo‐diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.
Core Ideas
Continuous genotypes offer a practical, computationally efficient, and biologically realistic approach for modeling genomic prediction in highly polyploid crops such as sugarcane, effectively accommodating their inherent complexity.
Continuous genotypes result in more accurate predictions, demonstrating slightly improved prediction accuracy compared to diploid parameterization.
The effect of continuous genotypes on prediction accuracy may vary depending on the specific trait being predicted. It is crucial to consider the trait‐specific variation when building prediction models.